wehub-resource-sync
449 lines
12 KiB
Python
449 lines
12 KiB
Python
# pylint: disable=invalid-name, unused-import
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"""Runtime NDArray api"""
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from __future__ import absolute_import
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import ctypes
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import sys
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import numpy as np
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from .base import _FFI_MODE, _LIB, c_array, c_str, check_call, string_types
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from .runtime_ctypes import (
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dgl_shape_index_t,
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DGLArray,
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DGLArrayHandle,
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DGLContext,
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DGLDataType,
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TypeCode,
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)
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IMPORT_EXCEPT = RuntimeError if _FFI_MODE == "cython" else ImportError
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try:
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# pylint: disable=wrong-import-position
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if _FFI_MODE == "ctypes":
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raise ImportError()
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if sys.version_info >= (3, 0):
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from ._cy3.core import (
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_from_dlpack,
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_make_array,
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_reg_extension,
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_set_class_ndarray,
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NDArrayBase as _NDArrayBase,
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)
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else:
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from ._cy2.core import (
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_from_dlpack,
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_make_array,
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_reg_extension,
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_set_class_ndarray,
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NDArrayBase as _NDArrayBase,
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)
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except IMPORT_EXCEPT:
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# pylint: disable=wrong-import-position
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from ._ctypes.ndarray import (
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_from_dlpack,
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_make_array,
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_reg_extension,
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_set_class_ndarray,
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NDArrayBase as _NDArrayBase,
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)
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def context(dev_type, dev_id=0):
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"""Construct a DGL context with given device type and id.
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Parameters
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----------
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dev_type: int or str
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The device type mask or name of the device.
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dev_id : int, optional
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The integer device id
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Returns
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-------
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ctx: DGLContext
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The corresponding context.
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Examples
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--------
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Context can be used to create reflection of context by
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string representation of the device type.
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.. code-block:: python
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assert dgl.context("cpu", 1) == dgl.cpu(1)
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assert dgl.context("gpu", 0) == dgl.gpu(0)
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assert dgl.context("cuda", 0) == dgl.gpu(0)
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"""
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if isinstance(dev_type, string_types):
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dev_type = dev_type.split()[0]
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if dev_type not in DGLContext.STR2MASK:
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raise ValueError("Unknown device type %s" % dev_type)
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dev_type = DGLContext.STR2MASK[dev_type]
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return DGLContext(dev_type, dev_id)
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def numpyasarray(np_data):
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"""Return a DGLArray representation of a numpy array."""
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data = np_data
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assert data.flags["C_CONTIGUOUS"]
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arr = DGLArray()
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shape = c_array(dgl_shape_index_t, data.shape)
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arr.data = data.ctypes.data_as(ctypes.c_void_p)
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arr.shape = shape
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arr.strides = None
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arr.dtype = DGLDataType(np.dtype(data.dtype).name)
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arr.ndim = data.ndim
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# CPU device
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arr.ctx = context(1, 0)
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return arr, shape
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def empty(shape, dtype="float32", ctx=context(1, 0)):
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"""Create an empty array given shape and device
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Parameters
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----------
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shape : tuple of int
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The shape of the array
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dtype : type or str
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The data type of the array.
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ctx : DGLContext
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The context of the array
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Returns
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-------
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arr : dgl.nd.NDArray
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The array dgl supported.
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"""
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shape = c_array(dgl_shape_index_t, shape)
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ndim = ctypes.c_int(len(shape))
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handle = DGLArrayHandle()
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dtype = DGLDataType(dtype)
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check_call(
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_LIB.DGLArrayAlloc(
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shape,
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ndim,
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ctypes.c_int(dtype.type_code),
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ctypes.c_int(dtype.bits),
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ctypes.c_int(dtype.lanes),
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ctx.device_type,
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ctx.device_id,
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ctypes.byref(handle),
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)
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)
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return _make_array(handle, False)
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def empty_shared_mem(name, is_create, shape, dtype="float32"):
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"""Create an empty array with shared memory given shape and dtype
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Parameters
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----------
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name : string
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The name of the shared memory. It's a file name in Unix.
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is_create : bool
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Whether to create the shared memory or use the one created by somewhere else.
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shape : tuple of int
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The shape of the array
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dtype : type or str
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The data type of the array.
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Returns
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-------
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arr : dgl.nd.NDArray
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The array dgl supported.
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"""
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name = ctypes.c_char_p(name.encode("utf-8"))
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shape = c_array(dgl_shape_index_t, shape)
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ndim = ctypes.c_int(len(shape))
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handle = DGLArrayHandle()
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dtype = DGLDataType(dtype)
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check_call(
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_LIB.DGLArrayAllocSharedMem(
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name,
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shape,
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ndim,
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ctypes.c_int(dtype.type_code),
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ctypes.c_int(dtype.bits),
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ctypes.c_int(dtype.lanes),
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is_create,
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ctypes.byref(handle),
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)
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)
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return _make_array(handle, False)
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def from_dlpack(dltensor):
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"""Produce an array from a DLPack tensor without memory copy.
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Retrieves the underlying DLPack tensor's pointer to create an array from the
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data. Removes the original DLPack tensor's destructor as now the array is
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responsible for destruction.
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Parameters
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----------
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dltensor : DLPack tensor
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Input DLManagedTensor, can only be consumed once.
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Returns
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-------
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arr: dgl.nd.NDArray
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The array view of the tensor data.
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"""
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return _from_dlpack(dltensor)
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class NDArrayBase(_NDArrayBase):
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"""A simple Device/CPU Array object in runtime."""
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@property
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def shape(self):
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"""Shape of this array"""
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return tuple(
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self.handle.contents.shape[i]
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for i in range(self.handle.contents.ndim)
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)
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@property
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def dtype(self):
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"""Type of this array"""
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return str(self.handle.contents.dtype)
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@property
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def ctx(self):
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"""context of this array"""
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return self.handle.contents.ctx
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@property
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def context(self):
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"""context of this array"""
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return self.ctx
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def __hash__(self):
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return ctypes.cast(self.handle, ctypes.c_void_p).value
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def __eq__(self, other):
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return self.same_as(other)
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def __ne__(self, other):
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return not self.__eq__(other)
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def same_as(self, other):
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"""Check object identity equality
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Parameters
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----------
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other : object
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The other object to compare to
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Returns
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-------
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same : bool
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Whether other is same as self.
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"""
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if not isinstance(other, NDArrayBase):
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return False
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return self.__hash__() == other.__hash__()
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def __setitem__(self, in_slice, value):
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"""Set ndarray value"""
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if (
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not isinstance(in_slice, slice)
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or in_slice.start is not None
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or in_slice.stop is not None
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):
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raise ValueError("Array only support set from numpy array")
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if isinstance(value, NDArrayBase):
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if value.handle is not self.handle:
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value.copyto(self)
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elif isinstance(value, (np.ndarray, np.generic)):
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self.copyfrom(value)
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else:
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raise TypeError("type %s not supported" % str(type(value)))
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def copyfrom(self, source_array):
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"""Perform a synchronized copy from the array.
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Parameters
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----------
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source_array : array_like
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The data source we should like to copy from.
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Returns
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-------
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arr : NDArray
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Reference to self.
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"""
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if isinstance(source_array, NDArrayBase):
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source_array.copyto(self)
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return self
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if not isinstance(source_array, np.ndarray):
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try:
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source_array = np.asarray(source_array, dtype=self.dtype)
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except:
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raise TypeError(
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"array must be an array_like data,"
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+ "type %s is not supported" % str(type(source_array))
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)
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t = DGLDataType(self.dtype)
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shape, dtype = self.shape, self.dtype
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if t.lanes > 1:
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shape = shape + (t.lanes,)
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t.lanes = 1
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dtype = str(t)
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if source_array.shape != shape:
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raise ValueError(
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"array shape do not match the shape of NDArray {0} vs {1}".format(
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source_array.shape, shape
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)
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)
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source_array = np.ascontiguousarray(source_array, dtype=dtype)
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assert source_array.flags["C_CONTIGUOUS"]
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data = source_array.ctypes.data_as(ctypes.c_void_p)
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nbytes = ctypes.c_size_t(
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source_array.size * source_array.dtype.itemsize
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)
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check_call(_LIB.DGLArrayCopyFromBytes(self.handle, data, nbytes))
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return self
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def __repr__(self):
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res = "dgl.{0}@{1}".format(self.asnumpy().__repr__(), self.context)
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return res
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def __str__(self):
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return str(self.asnumpy())
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def asnumpy(self):
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"""Convert this array to numpy array
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Returns
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-------
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np_arr : numpy.ndarray
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The corresponding numpy array.
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"""
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t = DGLDataType(self.dtype)
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shape, dtype = self.shape, self.dtype
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if t.lanes > 1:
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shape = shape + (t.lanes,)
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t.lanes = 1
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dtype = str(t)
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np_arr = np.empty(shape, dtype=dtype)
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assert np_arr.flags["C_CONTIGUOUS"]
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data = np_arr.ctypes.data_as(ctypes.c_void_p)
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nbytes = ctypes.c_size_t(np_arr.size * np_arr.dtype.itemsize)
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check_call(_LIB.DGLArrayCopyToBytes(self.handle, data, nbytes))
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return np_arr
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def copyto(self, target):
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"""Copy array to target
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Parameters
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----------
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target : NDArray
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The target array to be copied, must have same shape as this array.
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"""
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if isinstance(target, DGLContext):
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target = empty(self.shape, self.dtype, target)
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if isinstance(target, NDArrayBase):
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check_call(_LIB.DGLArrayCopyFromTo(self.handle, target.handle))
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else:
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raise ValueError("Unsupported target type %s" % str(type(target)))
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return target
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def pin_memory_(self):
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"""Pin host memory and map into GPU address space (in-place)"""
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check_call(_LIB.DGLArrayPinData(self.handle))
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def unpin_memory_(self):
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"""Unpin host memory pinned by pin_memory_()"""
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check_call(_LIB.DGLArrayUnpinData(self.handle))
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def record_stream(self, stream):
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"""Record the stream that is using this tensor.
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Note
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----
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This API is more for testing. Users should call ``record_stream``
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on torch.Tensor or dgl.graph directly.
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Parameters
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----------
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stream : DGLStreamHandle
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"""
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check_call(_LIB.DGLArrayRecordStream(self.handle, stream))
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def free_extension_handle(handle, type_code):
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"""Free c++ extension type handle
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Parameters
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----------
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handle : ctypes.c_void_p
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The handle to the extension type.
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type_code : int
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The tyoe code
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"""
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check_call(_LIB.DGLExtTypeFree(handle, ctypes.c_int(type_code)))
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def register_extension(cls, fcreate=None):
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"""Register a extension class to DGL.
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After the class is registered, the class will be able
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to directly pass as Function argument generated by DGL.
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Parameters
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----------
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cls : class
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The class object to be registered as extension.
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Note
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----
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The registered class is requires one property: _dgl_handle and a class attribute _dgl_tcode.
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- ```_dgl_handle``` returns integer represents the address of the handle.
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- ```_dgl_tcode``` gives integer represents type code of the class.
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Returns
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-------
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cls : class
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The class being registered.
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fcreate : function, optional
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The creation function to create a class object given handle value.
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Example
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-------
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The following code registers user defined class
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MyTensor to be DLTensor compatible.
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.. code-block:: python
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@dgl.register_extension
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class MyTensor(object):
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_dgl_tcode = dgl.TypeCode.ARRAY_HANDLE
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def __init__(self):
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self.handle = _LIB.NewDLTensor()
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@property
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def _dgl_handle(self):
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return self.handle.value
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"""
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if fcreate and cls._dgl_tcode < TypeCode.EXT_BEGIN:
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raise ValueError(
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"Cannot register create when extension tcode is same as buildin"
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)
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_reg_extension(cls, fcreate)
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return cls
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